Kevin James, NPS I&M Heartland Network

1. Using Advanced Satellite Products to Better Understand I&M Data within the Context of the Larger Ecoregion Kevin James, NPS I&M Heartland Network Jeff Morisette, USGS Fort Collins Science Centerw/ Colin Talbert, USGS Fortand Pete Ma, NASA Goddard Space Flight Center

2. Background… • “Parks are part of larger ecological systems and must be managed in that context” (Fancy, Gross, & Carter, 2008). • Work funded through the USGS/NPS National Park Monitoring Project. Starting third year of funding. • Primary objective: help park managers use cutting edge moderate- (250m) and high- (30m) resolution remote sensing products to place I&M observations within the context of the larger ecosystem.

3. Background… • The foundation for this work is existing and freely available remote sensing data products: • NASA-funded 250m spatial resolution land surface phenology product for North America • Landsat data available from USGS for free. • These products represent a significant national investment in ecosystem monitoring.

4. Objectives: • Characterize phenology across the Southern Plains, Heartland, and Northern Great Plains Networks from 2000 on. • Building on the remote sensed vegetation indices and available Network data, identify appropriate sampling periods and locations that maximize information content for vegetation vital signs monitoring. • Evaluate the impact of management actions in light of intra- and inter-annual vegetation and climate variability.

5. Initial parks: selection criteria • Proposal focused on three networks: Southern Plains, Northern Great Plains, and Heartland Network • We wanted at least one park per network • Fairly large in spatial extent • Existing data from either I&M or fire monitoring programs • Fairly native grassland

7. Theodore Roosevelt NP

8. Windcave NP

9. Tallgrass Prairie NP

10. Chickasaw NRA

11. Lake Meredith NRA

12. MODIS land surface phenology data

13. Modified TIMESAT Parameters Beginning of season End of season Length of season Base value Peak time Peak value Amplitude Left derivative Right derivative Integral over season - absolute Integral over season - scaled Maximum value Minimum value Mean value Root Mean Square Error Phenology Product User Guide, Tan et alhttp://accweb.nascom.nasa.gov/project/docs/User_guide_PHN.pdf.

14. Web-Enabled Landsat Data (WELD) Monthly composites…

15. Displaying the imagery

17. ArcGIS Explorer application

19. Annual variations in species richness

20. Annual variation in and out of TAPR

21. Annual variations in species richness at TAPR

22. Variations in grassland obligates at TAPR

23. A (very) preliminary model for bird species richness • An ensemble model* using • Maxent • Boosted Regression Trees • Logistic Regression • MARS • Random Forest * Following Stohlgren TJ, P. Ma, S. Kumar, M. Rocca, J.T. Morisette, C.S. Jarnevich, and N. Benson. 2010. Ensemble habitat mapping of invasive plant species., Risk Analysis, 30(2)224-235.

24. Future work • Develop a standard operating procedure (SOP) for integrating MODIS Phenology Data into the I&M program. • Correlative analysis connecting the I&M data to these remote sensing variables • Analysis of comparing phenology metrics inside and outside the park but with the stratification based on land cover type.